The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
Dataset 
No. of used points 
RMSE-E (m) 
RMSE-X(m) 
RMSE-Y(m) 
RMSE-Z (m) 
Mean / Min / Max - E (m) 
Mean / Min / Max - X (m) 
Mean /Min /Max - Y(m) 
Mean / Min / Max - Z (m) 
Tx/Ty /Tz (m) 
Catalonia, DTM 
2870270 
3.14 
0.02 / -56 / 63 
1.33/-3.61 /-1.16 
0.62 
0.00 / -40 / 28 
0.75 
0.01 /-35/60 
2.98 
0.00/-51 /55 
Sakurajima, DSM 
394962 
9.26 
1.00 /-160/ 153 
1.41 /-2.63/-1.09 
4.68 
-0.14/-66/ 156 
3.77 
-0.il/-65/51 
7.04 
0.61 /-66/ 135 
Table 5. Statistical values of the Euclidean distance (E) differences between reference DSM (in Sakurajima) and DTM (in Catalonia) 
and matching DSM and values of the shift parameters (T) between the reference data and the Cartosat-1 DSM. 
Figure 8. Left: Orthophoto of the area used for the evaluation of the DSM generation with reference data. The black area is the 
excluded cloud area. Right: Colour coded image of the Euclidian distances (DSM - reference). The color intervals correspond to 15 
m. The white circle defines a critical area shown in detail in Figure 6. The other reddish areas around the cloud area are also due to 
shadows and mainly very large perspective differences. 
A color coded and shaded view of the generated DSM is 
presented in Figure 7. Figure 8 shows an orthophoto of the area 
with the typical structure of a volcano and the large shadow 
areas. One of the two calderas had to be excluded because of 
occlusion by clouds. 
The results of the Cartosat-1 DSM valuation are summarized in 
Table 5 and visualized in Figure 8. The big blunders and the 
resulting worse accuracy are partly due to the steep volcano 
surface with terrain cuts, partly also having shadows and/or 
causing occlusions, low texture and large perspective 
differences. Additionally, there are also blunders around the 
excluded cloud area (see Figure 8). The larger sigma X and Y 
values in Table 5, compared to the Catalonia dataset, are due to 
the mountain slopes, with inclined Euclidean distance errors 
projected more in planimetry. 
6. CONCLUSIONS 
Regarding image quality, these Cartosat-1 images were better 
than other ones used in previous tests. The major remaining 
problems are the interlacing noise and the blurring of the Fore 
channel. 
Regarding the necessary RPC refinement and the accuracy 
potential for 3D point measurement, the following can be 
concluded. RPCs should be corrected by an affine 
transformation, shifts alone do not suffice. The shifts with or 
without affine terms were much smaller than in previous tests, 
showing an improved absolute geolocation accuracy. The GCP 
distribution, although for many high resolution satellite sensors 
(e.g. Ikonos), is not so important, for Cartosat-1 seems to have 
an influence on the accuracy, especially in the planimetry. Thus, 
to be on the safe side, a good GCP distribution is recommended. 
The number of the GCPs is not so crucial. For an affine 
correction of the RPCs and a certain redundancy, we 
recommend the use of about 6 GCPs as minimum. Despite the 
suboptimal B/H ratio, the height accuracy in pixels was 
exceptionally good, even exceeding previous results achieved 
with sensors like IKONOS. This indicates that the errors in the 
planimetric positioning are rather due to the poor identification 
of the GCPs.